System and method for analyzing and deducing criteria-related content for evaluation

ABSTRACT

A method, system and computer-usable medium are disclosed for analyzing and deducing criteria-related content for evaluation in a system capable of answering questions. A criteria text is processed to identify criteria. The criteria text is then analyzed to identify a set of criteria-related content associated with the criteria, which in turn is processed to determine its relationship to the criteria. Once the relationship has been determined, the set of criteria-related content is annotated accordingly. The set of criteria-related content is then used in accordance with its annotation when processing the criteria for evaluation.

This is a continuation of U.S. patent application Ser. No. 14/554,481,filed Nov. 26, 2014, entitled “System and Method for Analyzing andDeducing Criteria-Related Content for Evaluation,” which is incorporatedherein by reference in its entirety.

BACKGROUND OF THE INVENTION

Field of the Invention

The present invention relates in general to the field of computers andsimilar technologies, and in particular to software utilized in thisfield. Still more particularly, it relates to a method, system andcomputer-usable medium for analyzing and deducing criteria-relatedcontent for evaluation.

Description of the Related Art

When patients are seen, treated, or tested by medical practitioners andclinicians, the events of the interaction are recorded and become partof the patient's medical records. Maintenance of such medical records isan important element of modern medical treatment. Recently, thetechnology used for recording and archiving medical records has beenundergoing an evolution. Modern medical and health care institutions arenow adopting electronic medical records systems instead of traditionalpaper recording systems. Such computerized record keeping systems offersignificant advantages to the practitioners, the patient, and thehealthcare system as a whole.

Many medical and healthcare institutions also maintain a set oftreatment guidelines for clinical trials or other guideline-basedsystems. These guidelines typically include established criteria, whichare usually the product of long-term clinical studies, the results ofwhich are peer reviewed and published in established medical journals.Such criteria are often written with clarifications, restrictions anddefinitions, which may augment the criteria or can be ignored. As anexample, a clinical trial may be conducted that includes women ofchildbearing potential and men who do not practice contraception, withnon-childbearing is defined as =>1 year postmenopausal or surgicallysterilized. In this example, the definition of non-childbearing is acriteria clarification that can be excluded during criteria processing.

Conversely, certain criteria may contain clarifications or definitionsthat should be treated as an augmentation to the criteria duringprocessing. For example, a criteria for a clinical trial may state thatuncontrolled hypothermia (blast>=20, no hemoglobin medicine) should notbe eligible for this treatment. While hypothermia is typically definedas a blast>15, the clarification within the criteria states thatblast>=20 is the definition for uncontrolled hypothermia in the trial.As a result, the clarification is an augmentation to the criteria, whichshould be included when the criteria is processed. However, currentsystems lack the ability to deduce when these clarifications,restrictions and definitions should either be ignored or treated as anaugmentation when the criteria is processed. Furthermore, this inabilitymay skew how a given criteria is evaluated by a machine.

SUMMARY OF THE INVENTION

A method, system and computer-usable medium are disclosed for analyzingand deducing criteria-related content for evaluation in a system capableof answering questions. In various embodiments, a criteria text isprocessed to identify criteria. The criteria text is then analyzed toidentify a set of criteria-related content associated with the criteria,which in turn is processed to determine its relationship to thecriteria. Once the relationship has been determined, the set ofcriteria-related content is annotated accordingly. The set ofcriteria-related content is then used in accordance with its annotationwhen processing the criteria for evaluation.

In various embodiments, the set of criteria-related content isidentified by a trigger associated with the criteria. In theseembodiments, the trigger may be a set of parentheses, a set of brackets,a colon, a line indent, or a definition. In certain embodiments, therelationship of the set of criteria-related content to the criteria maybe a statement, a definition, a clarification, a restriction, a formula,a range of values, or a list. In various embodiments, the annotation ofthe set of criteria-related content may be “augmentation content” or“ignored content.” In certain embodiments, the criteria is associatedwith elements of a medical trial.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention may be better understood, and its numerousobjects, features and advantages made apparent to those skilled in theart by referencing the accompanying drawings. The use of the samereference number throughout the several figures designates a like orsimilar element.

FIG. 1 depicts an exemplary client computer in which the presentinvention may be implemented;

FIG. 2 is a simplified block diagram of an information handling systemcapable of performing computing operations;

FIG. 3 is a generalized depiction of criteria-related contentdeductions;

FIG. 4 shows the display of criteria and criteria-related content thathas been visualized in error within a user interface; and

FIGS. 5A through 5D are a generalized flowchart of the performance ofcriteria-related content deduction operations.

DETAILED DESCRIPTION

A method, system and computer-usable medium are disclosed for analyzingand deducing criteria-related content for evaluation in a system capableof answering questions. The present invention may be a system, a method,and/or a computer program product. In addition, selected aspects of thepresent invention may take the form of an entirely hardware embodiment,an entirely software embodiment (including firmware, resident software,micro-code, etc.) or an embodiment combining software and/or hardwareaspects that may all generally be referred to herein as a “circuit,”“module” or “system.” Furthermore, aspects of the present invention maytake the form of computer program product embodied in a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a dynamic or static random access memory(RAM), a read-only memory (ROM), an erasable programmable read-onlymemory (EPROM or Flash memory), a magnetic storage device, a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server or cluster of servers. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

FIG. 1 depicts a schematic diagram of one illustrative embodiment of aquestion prioritization system 10 and question/answer (QA) system 100connected to a computer network 140. The QA system 100 includes aknowledge manager 104 that is connected to a knowledge base 106 andconfigured to provide question/answer (QA) generation functionality forone or more content users who submit across the network 140 to the QAsystem 100. To assist with efficient sorting and presentation ofquestions to the QA system 100, the prioritization system 10 may beconnected to the computer network 140 to receive user questions, and mayinclude a plurality of subsystems which interact with cognitive systems,like the knowledge manager 100, to prioritize questions or requestsbeing submitted to the knowledge manager 100.

The Named Entity subsystem 12 receives and processes each question 11 byusing natural language (NL) processing to analyze each question andextract question topic information contained in the question, such asnamed entities, phrases, urgent terms, and/or other specified termswhich are stored in one or more domain entity dictionaries 13. Byleveraging a plurality of pluggable domain dictionaries relating todifferent domains or areas (e.g., travel, healthcare, electronics, gameshows, financial services), the domain dictionary 11 enables criticaland urgent words (e.g., “threat level”) from different domains (e.g.,“travel”) to be identified in each question based on their presence inthe domain dictionary 11. To this end, the Named Entity subsystem 12 mayuse a Natural Language Processing (NLP) routine to identify the questiontopic information in each question. As used herein, “NLP” refers to thefield of computer science, artificial intelligence, and linguisticsconcerned with the interactions between computers and human (natural)languages. In this context, NLP is related to the area of human-computerinteraction and natural language understanding by computer systems thatenable computer systems to derive meaning from human or natural languageinput. For example, NLP can be used to derive meaning from ahuman-oriented question such as, “What is tallest mountain in NorthAmerica?” and to identify specified terms, such as named entities,phrases, or urgent terms contained in the question. The processidentifies key terms and attributes in the question and compares theidentified terms to the stored terms in the domain dictionary 13.

The Question Priority Manager subsystem 14 performs additionalprocessing on each question to extract question context information 15A.In addition or in the alternative, the Question Priority Managersubsystem 14 may also extract server performance information 15B for thequestion prioritization system 10 and/or QA system 100. In selectedembodiments, the extracted question context information 15A may includedata that identifies the user context and location when the question wassubmitted or received. For example, the extracted question contextinformation 15A may include data that identifies the user who submittedthe question (e.g., through login credentials), the device or computerwhich sent the question, the channel over which the question wassubmitted, the location of the user or device that sent the question,any special interest location indicator (e.g., hospital, public-safetyanswering point, etc.), or other context-related data for the question.The Question Priority Manager subsystem 14 may also determine or extractselected server performance data 15B for the processing of eachquestion. In selected embodiments, the server performance information15B may include operational metric data relating to the availableprocessing resources at the question prioritization system 10 and/or QAsystem 100, such as operational or run-time data, CPU utilization data,available disk space data, bandwidth utilization data, etc. As part ofthe extracted information 15A/B, the Question Priority Manager subsystem14 may identify the SLA or QoS processing requirements that apply to thequestion being analyzed, the history of analysis and feedback for thequestion or submitting user, and the like. Using the question topicinformation and extracted question context and/or server performanceinformation, the Question Priority Manager subsystem 14 is configured topopulate feature values for the Priority Assignment Model 16 whichprovides a machine learning predictive model for generating a targetpriority values for the question, such as by using an artificialintelligence (AI) rule-based logic to determine and assign a questionurgency value to each question for purposes of prioritizing the responseprocessing of each question by the QA system 100.

The Prioritization Manager subsystem 17 performs additional sort or rankprocessing to organize the received questions based on at least theassociated target priority values such that high priority questions areput to the front of a prioritized question queue 18 for output asprioritized questions 19. In the question queue 18 of the PrioritizationManager subsystem 17, the highest priority question is placed at thefront for delivery to the assigned QA system 100. In selectedembodiments, the prioritized questions 19 from the PrioritizationManager subsystem 17 that have a specified target priority value may beassigned to a specific pipeline (e.g., QA System 100A) in the QA systemcluster 100. As will be appreciated, the Prioritization Managersubsystem 17 may use the question queue 18 as a message queue to providean asynchronous communications protocol for delivering prioritizedquestions 19 to the QA system 100 such that the Prioritization Managersubsystem 17 and QA system 100 do not need to interact with a questionqueue 18 at the same time by storing prioritized questions in thequestion queue 18 until the QA system 100 retrieves them. In this way, awider asynchronous network supports the passing of prioritized questionsas messages between different computer systems 100A, 100B, connectingmultiple applications and multiple operating systems. Messages can alsobe passed from queue to queue in order for a message to reach theultimate desired recipient. An example of a commercial implementation ofsuch messaging software is IBM's WebSphere MQ (previously MQ Series). Inselected embodiments, the organizational function of the PrioritizationManager subsystem 17 may be configured to convert over-subscribingquestions into asynchronous responses, even if they were asked in asynchronized fashion.

The QA system 100 may include one or more QA system pipelines 100A,100B, each of which includes a computing device 104 (comprising one ormore processors and one or more memories, and potentially any othercomputing device elements generally known in the art including buses,storage devices, communication interfaces, and the like) for processingquestions received over the network 140 from one or more users atcomputing devices (e.g., 110, 120, 130) connected over the network 140for communication with each other and with other devices or componentsvia one or more wired and/or wireless data communication links, whereeach communication link may comprise one or more of wires, routers,switches, transmitters, receivers, or the like. In this networkedarrangement, the QA system 100 and network 140 may enablequestion/answer (QA) generation functionality for one or more contentusers. Other embodiments of QA system 100 may be used with components,systems, sub-systems, and/or devices other than those that are depictedherein.

In each QA system pipeline 100A, 100B, a prioritized question 19 isreceived and prioritized for processing to generate an answer 20. Insequence, prioritized questions 19 are dequeued from the shared questionqueue 18, from which they are dequeued by the pipeline instances forprocessing in priority order rather than insertion order. In selectedembodiments, the question queue 18 may be implemented based on a“priority heap” data structure. During processing within a QA systempipeline (e.g., 100A), questions may be split into many subtasks whichrun concurrently. A single pipeline instance can process a number ofquestions concurrently, but only a certain number of subtasks. Inaddition, each QA system pipeline may include a prioritized queue (notshown) to manage the processing order of these subtasks, with thetop-level priority corresponding to the time that the correspondingquestion started (earliest has highest priority). However, it will beappreciated that such internal prioritization within each QA systempipeline may be augmented by the external target priority valuesgenerated for each question by the Question Priority Manager subsystem14 to take precedence or ranking priority over the question start time.In this way, more important or higher priority questions can “fasttrack” through the QA system pipeline if it is busy with already-runningquestions.

In the QA system 100, the knowledge manager 104 may be configured toreceive inputs from various sources. For example, knowledge manager 104may receive input from the question prioritization system 10, network140, a knowledge base or corpus of electronic documents 106 or otherdata, a content creator 108, content users, and other possible sourcesof input. In selected embodiments, some or all of the inputs toknowledge manager 104 may be routed through the network 140 and/or thequestion prioritization system 10. The various computing devices (e.g.,110, 120, 130, 150, 160, 170) on the network 140 may include accesspoints for content creators and content users. Some of the computingdevices may include devices for a database storing the corpus of data asthe body of information used by the knowledge manager 104 to generateanswers to cases. The network 140 may include local network connectionsand remote connections in various embodiments, such that knowledgemanager 104 may operate in environments of any size, including local andglobal, e.g., the Internet. Additionally, knowledge manager 104 servesas a front-end system that can make available a variety of knowledgeextracted from or represented in documents, network-accessible sourcesand/or structured data sources. In this manner, some processes populatethe knowledge manager with the knowledge manager also including inputinterfaces to receive knowledge requests and respond accordingly.

In one embodiment, the content creator creates content in a document 106for use as part of a corpus of data with knowledge manager 104. Thedocument 106 may include any file, text, article, or source of data(e.g., scholarly articles, dictionary definitions, encyclopediareferences, and the like) for use in knowledge manager 104. Contentusers may access knowledge manager 104 via a network connection or anInternet connection to the network 140, and may input questions toknowledge manager 104 that may be answered by the content in the corpusof data. As further described below, when a process evaluates a givensection of a document for semantic content, the process can use avariety of conventions to query it from the knowledge manager. Oneconvention is to send a well-formed question. Semantic content iscontent based on the relation between signifiers, such as words,phrases, signs, and symbols, and what they stand for, their denotation,or connotation. In other words, semantic content is content thatinterprets an expression, such as by using Natural Language (NL)Processing. In one embodiment, the process sends well-formed questions(e.g., natural language questions, etc.) to the knowledge manager.Knowledge manager 104 may interpret the question and provide a responseto the content user containing one or more answers to the question. Insome embodiments, knowledge manager 104 may provide a response to usersin a ranked list of answers.

In some illustrative embodiments, QA system 100 may be the IBM Watson™QA system available from International Business Machines Corporation ofArmonk, New York, which is augmented with the mechanisms of theillustrative embodiments described hereafter. The IBM Watson™ knowledgemanager system may receive an input question which it then parses toextract the major features of the question, that in turn are then usedto formulate queries that are applied to the corpus of data. Based onthe application of the queries to the corpus of data, a set ofhypotheses, or candidate answers to the input question, are generated bylooking across the corpus of data for portions of the corpus of datathat have some potential for containing a valuable response to the inputquestion.

The IBM Watson™ QA system then performs deep analysis on the language ofthe input prioritized question 19 and the language used in each of theportions of the corpus of data found during the application of thequeries using a variety of reasoning algorithms. There may be hundredsor even thousands of reasoning algorithms applied, each of whichperforms different analysis, e.g., comparisons, and generates a score.For example, some reasoning algorithms may look at the matching of termsand synonyms within the language of the input question and the foundportions of the corpus of data. Other reasoning algorithms may look attemporal or spatial features in the language, while others may evaluatethe source of the portion of the corpus of data and evaluate itsveracity.

The scores obtained from the various reasoning algorithms indicate theextent to which the potential response is inferred by the input questionbased on the specific area of focus of that reasoning algorithm. Eachresulting score is then weighted against a statistical model. Thestatistical model captures how well the reasoning algorithm performed atestablishing the inference between two similar passages for a particulardomain during the training period of the IBM Watson™ QA system. Thestatistical model may then be used to summarize a level of confidencethat the IBM Watson™ QA system has regarding the evidence that thepotential response, i.e. candidate answer, is inferred by the question.This process may be repeated for each of the candidate answers until theIBM Watson™ QA system identifies candidate answers that surface as beingsignificantly stronger than others and thus, generates a final answer,or ranked set of answers, for the input question. The QA system 100 thengenerates an output response or answer 20 with the final answer andassociated confidence and supporting evidence. More information aboutthe IBM Watson™ QA system may be obtained, for example, from the IBMCorporation website, IBM Redbooks, and the like. For example,information about the IBM Watson™ QA system can be found in Yuan et al.,“Watson and Healthcare,” IBM developerWorks, 2011 and “The Era ofCognitive Systems: An Inside Look at IBM Watson and How it Works” by RobHigh, IBM Redbooks, 2012.

Types of information processing systems that can utilize QA system 100range from small handheld devices, such as handheld computer/mobiletelephone 110 to large mainframe systems, such as mainframe computer170. Examples of handheld computer 110 include personal digitalassistants (PDAs), personal entertainment devices, such as MP3 players,portable televisions, and compact disc players. Other examples ofinformation processing systems include pen, or tablet, computer 120,laptop, or notebook, computer 130, personal computer system 150, andserver 160. As shown, the various information processing systems can benetworked together using computer network 140. Types of computer network140 that can be used to interconnect the various information processingsystems include Local Area Networks (LANs), Wireless Local Area Networks(WLANs), the Internet, the Public Switched Telephone Network (PSTN),other wireless networks, and any other network topology that can be usedto interconnect the information processing systems. Many of theinformation processing systems include nonvolatile data stores, such ashard drives and/or nonvolatile memory. Some of the informationprocessing systems may use separate nonvolatile data stores (e.g.,server 160 utilizes nonvolatile data store 165, and mainframe computer170 utilizes nonvolatile data store 175). The nonvolatile data store canbe a component that is external to the various information processingsystems or can be internal to one of the information processing systems.An illustrative example of an information processing system showing anexemplary processor and various components commonly accessed by theprocessor is shown in FIG. 2.

FIG. 2 illustrates an information processing system 202, moreparticularly, a processor and common components, which is a simplifiedexample of a computer system capable of performing the computingoperations described herein. Information processing system 202 includesa processor unit 204 that is coupled to a system bus 206. A videoadapter 208, which controls a display 210, is also coupled to system bus206. System bus 206 is coupled via a bus bridge 212 to an Input/Output(I/O) bus 214. An I/O interface 216 is coupled to I/O bus 214. The I/Ointerface 216 affords communication with various I/O devices, includinga keyboard 218, a mouse 220, a Compact Disk-Read Only Memory (CD-ROM)drive 222, a floppy disk drive 224, and a flash drive memory 226. Theformat of the ports connected to I/O interface 216 may be any known tothose skilled in the art of computer architecture, including but notlimited to Universal Serial Bus (USB) ports.

The information processing system 202 is able to communicate with aservice provider server 252 via a network 228 using a network interface230, which is coupled to system bus 206. Network 228 may be an externalnetwork such as the Internet, or an internal network such as an EthernetNetwork or a Virtual Private Network (VPN). Using network 228, clientcomputer 202 is able to use the present invention to access serviceprovider server 252.

A hard drive interface 232 is also coupled to system bus 206. Hard driveinterface 232 interfaces with a hard drive 234. In a preferredembodiment, hard drive 234 populates a system memory 236, which is alsocoupled to system bus 206. Data that populates system memory 236includes the information processing system's 202 operating system (OS)238 and software programs 244.

OS 238 includes a shell 240 for providing transparent user access toresources such as software programs 244. Generally, shell 240 is aprogram that provides an interpreter and an interface between the userand the operating system. More specifically, shell 240 executes commandsthat are entered into a command line user interface or from a file.Thus, shell 240 (as it is called in UNIX®), also called a commandprocessor in Windows®, is generally the highest level of the operatingsystem software hierarchy and serves as a command interpreter. The shellprovides a system prompt, interprets commands entered by keyboard,mouse, or other user input media, and sends the interpreted command(s)to the appropriate lower levels of the operating system (e.g., a kernel242) for processing. While shell 240 generally is a text-based,line-oriented user interface, the present invention can also supportother user interface modes, such as graphical, voice, gestural, etc.

As depicted, OS 238 also includes kernel 242, which includes lowerlevels of functionality for OS 238, including essential servicesrequired by other parts of OS 238 and software programs 244, includingmemory management, process and task management, disk management, andmouse and keyboard management. Software programs 244 may include abrowser 246 and email client 248. Browser 246 includes program modulesand instructions enabling a World Wide Web (WWW) client (i.e.,information processing system 202) to send and receive network messagesto the Internet using HyperText Transfer Protocol (HTTP) messaging, thusenabling communication with service provider server 252. In variousembodiments, software programs 244 may also include a criteria deductionsystem 250. In these and other embodiments, the criteria deductionsystem 250 includes code for implementing the processes describedhereinbelow. In one embodiment, information processing system 202 isable to download the criteria deduction system 250 from a serviceprovider server 252.

The hardware elements depicted in the information processing system 202are not intended to be exhaustive, but rather are representative tohighlight components used by the present invention. For instance, theinformation processing system 202 may include alternate memory storagedevices such as magnetic cassettes, Digital Versatile Disks (DVDs),Bernoulli cartridges, and the like. These and other variations areintended to be within the spirit, scope and intent of the presentinvention.

FIG. 3 is a generalized depiction of criteria-related content deductionsimplemented in accordance with an embodiment of the invention. Invarious embodiments, a criteria or condition is evaluated to excludedefinitions, clarifications and restrictions such that they do not skewscoring and evaluation of the criteria. In these embodiments, deductionoperations are performed to determine whether individual definitions,clarifications and restrictions should be ignored or used to augment thecriteria, but not become a criterion or condition themselves. As usedherein, criteria broadly refer to treatment rules or principles relatedto a prospective medical treatment. As an example, an individualcriterion may have an associated value, such as the patient must be lessthan 60 years of age, or have a Body Mass Index (BMI) value that isgreater than 19 and less than 25.

In various embodiments, a body of criteria text is processed to identifycriteria. The criteria text is then further processed to recognize andannotate key characteristic triggers, such as parentheses, brackets,colons, line indents, or definitions following or preceding a statementthat may indicate a set of content related to various criteria. Incertain embodiments, the set of criteria-related content is in theproximity of an associated criterion in the criteria text.

In these and other embodiments, the set of criteria-related content mayinclude a statement, a definition, a clarification, a restriction, aformula, a range of values, or a list. In certain embodiments, the setof criteria-related content is processed to augment the criteria orcondition. In these embodiments, the set of criteria-related contentdoes not become a separate criteria or condition for evaluation. In oneembodiment, the augmentation is performed by appending the additionalcontent to the criteria or condition. The method used to perform theaugmentation or appending is a matter of design choice.

In various embodiments, the set of criteria-related content is comparedto known formulae, definitions, and key attribute values from standards,the results of which are then used to determine whether the set ofcriteria-related content should be ignored or categorized as a conditionor an argument. In certain embodiments, a definition within a set ofcriteria-related content is evaluated directly, instead of the originalstatement. As an example, a criteria may include “high-risk(intermediate-2 or high by IPSS r>10% blasts, including CMML).” In thisexample, the criteria-related content within the parentheses is adefinition that is subsequently used to evaluate the criterion. Asanother example, a criteria may include “MDS (Low, INT-1 by IPSS, orhypocellular).” In this example, the criteria-related content within theparentheses is not just a definition, but additional criteria as well.Skilled practitioners of the art will recognize that many suchembodiments are possible and the foregoing is not intended to limit thespirit, scope or intent of the invention.

Referring now to FIG. 3, a set of criteria text 300 contains criteria312, 332, 342, which is parsed to find key characteristic triggers 316,318, 336, 346, such as parentheses, brackets, colons, line indents, ordefinitions following or preceding a statement that may indicate a setof content related to various criteria. In various embodiments, thesetriggers serve as points of reference that are implemented to identifyan associated set of criteria-related content 320 338, 348, which isthen processed to determine whether it is annotated to be ignored 350 orused to augment 322, 334 the criteria during its evaluation.

In certain embodiments, if a phrase or sentence within the set ofcriteria-related content is determined to be a definition according to aUnified Medical Language System (UMLS) or a dictionary search, then itis annotated as a definition and is ignored when the criteria isevaluated. In various embodiments, if the set of criteria-relatedcontent contains attributes from UMLS, or as defined by a dictionarywith equality and values assigned, it is annotated to be used to augmentthe normal values associated with the criteria. In certain embodiments,if the criteria-related content contains mathematical values or ranges,they are checked against a formula data set, or standard sets, todetermine whether they match or are within range of expected values. Ifso, then the set of criteria-related content is annotated to be ignoredwhen the criteria is evaluated. If not, then the set of criteria-relatedcontent is annotated to be used to augment the normal values or rangesassociated with the criteria.

FIG. 4 shows the display of criteria and criteria-related content thathas been visualized in error within a user interface (UI) windowimplemented in accordance with an embodiment of the invention. In thisembodiment, a set of criteria text 404 is displayed within a UI window402, along with criteria-related content that has been visualized inerror 406 as a result of the invention not being implemented asdescribed in greater detail herein.

FIGS. 5A through 5D are a generalized flowchart of the performance ofcriteria-related content deduction operations implemented in accordancewith an embodiment of the invention. In this embodiment,criteria-related content deduction operations are begun in step 502,followed by the selection of a target criteria text in step 504.Delimiter markers (e.g., comma, semicolon, period, new line, etc.) arethen used in step 506 to parse the criteria text to identify criteria,followed by the selection of a delimited portion of the parsed criteriain step 508 for further parsing operations. The selected portion of theparsed criteria is then re-parsed in step 510 to identify keycharacteristic triggers described in greater detail herein.

A determination is then made in step 512 whether one or more triggerpoints have been identified. If so, then a set of trigger points isselected in step 514 to process, followed by annotating contentcorresponding to the set of trigger points as a set of criteria-relatedcontent in step 516. The set of criteria-related content is thenassociated with its corresponding criteria in step 518 for furtherevaluation. A determination is then made in step 520 whether to selectanother set of trigger points for processing. If so, the process iscontinued, proceeding with step 514. Otherwise, or if it was determinedin step 512 that no trigger points were identified, then a determinationis made in step 522 whether to select another delimited portion ofparsed criteria text to re-parse. If so, then then the process iscontinued, proceeding with step 508. Otherwise, a set ofcriteria-related content is selected in step 524 for processing.

The selected set of criteria-related content is then parsed and labeledfor identification in step 526, followed by the selection of a parsedportion of the set of criteria-related content in step 528 foradditional processing. The selected portion of the set ofcriteria-related content is then processed in step 530 to determinewhether is a statement, a formula, a range, or a list of values. Themethod by which the selected portion of the set of criteria-relatedcontent is processed to determine whether is a statement, a formula, arange, or a list of values is a matter a design choice.

A determination is then made in step 532 whether the selected portion ofthe set of criteria-related content is a statement. If so, then adetermination is made in step 534 whether the statement matches a knowndefinition. If so, then a determination is made in step 536 whether thestatement passes a dictionary check. If so, then the portion of the setof criteria-related content is categorized as “ignored content” viaannotation of its associated criteria or condition in step 538. Themethod by which it is determined that the statement matches a knowndefinition, and whether it passes the dictionary check, is a matter ofdesign choice.

However, if it was determined in step 532 that the portion of the set ofcriteria-related content was not a statement, then a determination ismade in step 540 whether the portion of the set of criteria-relatedcontent is a formula. If so, then a determination is made in step 542whether the formula matches a known formula store or Unified MedicalLanguage System (UMLS) formula. If so, then a determination is made instep 536 whether the values of the formula are different than thoseexpected. If so, then the portion of the set of criteria-related contentis categorized as “augmentation content” via annotation of itsassociated criteria or condition in step 546. The method by which it isdetermined that the formula matches a known formula, and whether thevalues of the formula are different than those expected, is a matter ofdesign choice.

However, if it was determined in step 540 that the portion of the set ofcriteria-related content was not a formula, then a determination is madein step 548 whether the portion of the set of criteria-related contentis a range of values. If so, then a determination is made in step 550whether the range of values deviates from a known range of standardvalues. If so, then a determination is made in step 552 whether therange of values is a variation of a known range of standard values. Ifso, then the portion of the set of criteria-related content iscategorized as “augmentation content” via annotation of its associatedcriteria or condition in step 554. The method by which it is determinedthat the range of values deviates from a known range of standard values,and whether the range of values is a variation of a known range ofstandard values, is a matter of design choice.

However, if it was determined in step 548 that the portion of the set ofcriteria-related content was not a range of values, then a determinationis made in step 556 whether the portion of the set of criteria-relatedcontent is a list of values. If so, then a determination is made in step558 whether the list of values deviates from a known list of standardvalues. If so, then a determination is made in step 560 whether therange of values is a variation of a known list of standard values. Ifso, then the portion of the set of criteria-related content iscategorized as “augmentation content” via annotation of its associatedcriteria or condition in step 554. The method by which it is determinedthat the list of values deviates from a known range of standard values,and whether the list of values is a variation of a known range ofstandard values, is a matter of design choice.

However, if it was determined in step 556 that the portion of the set ofcriteria-related content is a not a list of values, then the portion ofthe set of criteria-related content is categorized as “ignored content”via annotation of its associated criteria or condition in step 562.Thereafter, or if the determinations made in steps 534, 536, 542, 544,550, 552, 558 or 560, or the operations are completed in steps 538, 546,or 554, a determination is made in step 564 whether to select anotherparsed portion of the set of criteria-related content for processing. Ifso, then the process is continued, proceeding with step 528. If not,then a determination is made in step 566 whether to select another setof criteria-related content for processing. If so, then the process iscontinued, proceeding with step 524.

However, if it is determined in step 566 not to select another set ofcriteria-related content for processing, then the expected text,annotations and statements are processed in step 568 to generate a ListItem type to give key indicators. The criteria text, including sets ofcriteria-related content categorized as “augmentation content,” butexcluding sets of criteria-related content categorized as “ignoredcontent,” is then processed for evaluation in step 570. A determinationis then made in step 572 whether to end criteria-related contentdeduction operations. If not, then the process is continued, proceedingwith step 504. Otherwise, criteria-related content deduction operationsare ended in step 574.

Although the present invention has been described in detail, it shouldbe understood that various changes, substitutions and alterations can bemade hereto without departing from the spirit and scope of the inventionas defined by the appended claims.

What is claimed is:
 1. A computer-implemented method for analyzing anddeducing criteria-related content for evaluation via a criteriadeduction system executing on a hardware processor, the criteriadeduction system being associated with a question/answer (QA) systemcapable of answering questions, comprising: receiving an input corpus tothe criteria deduction system via a network, the input corpus comprisinga plurality of guidelines, each of the plurality of guidelinescomprising criteria, the criteria comprising principles related to eachof the plurality of guidelines and the criteria comprises a treatmentrule related to a prospective medical treatment; processing criteriatext from the plurality of guidelines to identify the criteria, theprocessing performed by the QA system capable of answering questions;analyzing, via the criteria deduction system, the criteria text toidentify a set of criteria-related content associated with the criteria,the criteria-related content comprising content that skews scoring andevaluation of the criteria when the QA system is answering a questionusing the criteria; processing, via the criteria deduction system, theset of criteria-related content to determine a relationship of thecriteria to an associated guideline; annotating, via the criteriadeduction system, the set of criteria-related content according to thedetermined relationship, wherein the annotation compromises ignoredcontent to identify content to be excluded from an evaluation of theprospective medical treatment; and augmentation content to adjust theevaluation of the prospective medical treatment; and using the set ofcriteria-related content in accordance with its annotation whenprocessing the criteria for the evaluation; and wherein, the set ofcriteria-related content is in proximity of an associated criterion inthe criteria text; and, the set of criteria-related content isidentified by a trigger associated with the criteria, the triggercomprising a key characteristic trigger, the key characteristic triggerserving as a point of reference without user intervention to identify anassociated set of criteria-related content.
 2. The method of claim 1,wherein the key characteristic trigger is a member of the set of: a setof parentheses; a set of brackets; a colon; a line indent; and adefinition.
 3. The method of claim 1, wherein the relationship is amember of the set of: a statement; a definition; a clarification; arestriction; a formula; a range of values; and a list.
 4. The method ofclaim 1, wherein the augmentation content comprising content that istreated as an augmentation to the criteria during criteria processing;and the ignored content ignored content comprising content that can beignored during criteria processing.
 5. The method of claim 4, whereinthe processing the criteria for evaluation comprises: excluding contentfrom an evaluation annotated to be ignored; and adjusting evaluation ofthe criteria based on augmentation content.